Methods to perform real-time analysis of fluid flow in a well, methods to perform real-time analysis of a well operation, and real-time well operation systems

ABSTRACT

A method to perform a real-time analysis of fluid flow in a well, includes receiving data simultaneously streamed from a plurality of sensors, and populating a model of the well with the data, where the model has a plurality of parameters that are inputs of the model, and where each parameter being associated with data streamed from a sensor of the plurality of sensors. The method also includes calculating a fluid flow of a fluid flowing through a location of the well based on the model. In response to receiving new data streamed from one or more sensors of the plurality of sensors, the method further includes dynamically calibrating the model in real time based on the new data and calculating an updated fluid flow of the fluid flowing through location of the well based on the recalibrated model.

BACKGROUND

The present disclosure relates generally to methods to perform real-time analysis of fluid flow in a well, methods to perform real-time analysis of a well operation, and real-time well operation systems.

Sensors are sometimes positioned at multiple positions of a hydrocarbon well to monitor fluid flow of fluids into and out of the well. Some of the sensors are positioned to monitor fluid flow of hydrocarbon resources to determine production rate and total production of hydrocarbon resources over time.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative embodiments of the present disclosure are described in detail below with reference to the attached drawing figures, which are incorporated by reference herein, and wherein:

FIG. 1 is a schematic, side view of a well environment where a real-time well operation system is deployed;

FIG. 2 is an illustration of a network of nodes positioned along multiples layers of a zone of a well;

FIG. 3 is a flow chart of a process to perform a real-time analysis of fluid flow in a well, and a real-time analysis of a well operation;

FIG. 4 is a block diagram of the real-time well operation system of FIG. 1 ;

FIG. 5 is another block diagram of the real-time well operation system of claim 1;

FIG. 6 is a flow chart of a process to perform a real-time analysis of fluid flow in a well; and

FIG. 7 is a flow chart of a process to perform a real-time analysis of a well operation. The illustrated figures are only exemplary and are not intended to assert or imply any limitation with regard to the environment, architecture, design, or process in which different embodiments may be implemented.

DETAILED DESCRIPTION

In the following detailed description of the illustrative embodiments, reference is made to the accompanying drawings that form a part hereof. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is understood that other embodiments may be utilized and that logical structural, mechanical, electrical, and chemical changes may be made without departing from the spirit or scope of the invention. To avoid detail not necessary to enable those skilled in the art to practice the embodiments described herein, the description may omit certain information known to those skilled in the art. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the illustrative embodiments is defined only by the appended claims.

The present disclosure relates to methods to perform real-time analysis of fluid flow in a well, methods to perform real-time analysis of a well operation, and real-time fluid flow analysis systems. A real-time well operation system receives data simultaneously streamed from multiple sensors that are disposed in or near a well and configured to provide data indicative of fluid flow (“fluid flow data”) and calculate fluid flow, status of hardware components and devices that are positioned in or near the well, and the status of one or more well operations that are performed at the well. Examples of fluid flow data include, but are not limited to, data indicative of the flow rate of the fluid, a composition of the fluid (e.g., oil, water), a direction of the fluid flow (such as uphole, downhole, into a device such as a valve, out of the device, from a first device to a second device, etc.), fluid pressure, a derivative of the fluid pressure over time, and other types of data indicative of the fluid flowing at one or more locations in or near the well. Examples of hardware components and devices include, but are not limited to, inflow control devices, valves, gauges, packers, tubulars, and other hardware components and devices that are deployed in or around the well.

The real-time well operation system populates a model of the well with the data. More particularly, the real-time well operation system simultaneously receives raw data from the sensors and utilizes the raw data as input parameters to populate and to recalibrate the model. In some embodiments, the real-time well operation system accumulates the raw data that is being simultaneously transmitted by the sensors, performs operations to reconcile the raw data, and populates and recalibrates the model while the raw data is being streamed. As referred to herein, a model of the well is a virtual representation of the well based on measurements made by one or more sensors and other types of hardware components of the well. In some embodiments, the model includes or is configured to provide up-to-date fluid flow data at various locations in or near the well, and data indicative of the status of hardware components and devices that are positioned in or near the well. In some embodiments, the model of the well is a digital twin of the well that is dynamically or periodically updated when the model is recalibrated. As referred to herein, a digital twin of a well is an evergreen numerical model of a physical object such as a location of a well, a section of the well, or the entire well. In some embodiments, the real-time well operation system performs edge-computing operations to generate and to recalibrate the model. As referred to herein, an edge-computing operation is a distributed computing operation that places computation and data storage near or at the well. In one or more of such embodiments, the real-time well operation system receives the raw data and performs calculations and other operations to generate the model and to recalibrate the model without first storing the raw data in a storage medium of the real-time well operation system. In some embodiments, the real-time well operation system also utilizes properties of hardware components as input parameters to generate and to recalibrate the model. Examples of properties of hardware components include, but are not limited to, the dimensions of flow control devices, valves, gauges, packers, tubulars, and other hardware components and devices that are deployed in or around the well, the locations and depths of the hardware components, dimensions of the outer diameters and inner diameters of the hardware components, as well as other physical, mechanical, electrical, and electromechanical properties of the hardware components.

The real-time well operation system calculates fluid flow properties of a fluid flowing through a desired location at or around the well based on the model. For example, where the desired location is the location that is a threshold number of feet below the surface and in a first zone of interest, the real-time well operation system utilizes the generated model to obtain the current flow rate, fluid composition, flow direction, and other fluid flow data at the location. In some embodiments, the fluid flow analysis system also determines the status of a hardware component at a desired location based on the model. For example, where the device is an inflow control device that is disposed in a second zone of interest, the real-time well operation system utilizes the generated model to determine fluid flow through the inflow control device, the operational status of the inflow control device, and whether the inflow control device is malfunctioning (e.g., not opening into a desired location, not closing, not opening at a desired rate, not closing at a desired rate, not providing desired fluid flow, or another type of malfunction). In some embodiments, the real-time well operation system receives the raw data and calculates the fluid flow based on the raw data without first storing the raw data in a storage medium of the real-time well operation system. Similarly in some embodiments, the real-time well operation system receives the raw data and determines the status of a hardware component at the desired location based on the raw data without first storing the raw data in a storage medium of the real-time well operation system. Additional descriptions of operations performed by the real-time well operation system to calculate fluid flow and to analyze hardware components and other devices disposed at or near the well are provided herein.

During a well operation, up-to-date data are continuously transmitted from the sensors to the real-time well operation system. The real-time well operation system, in response to receiving new data streamed from one or more sensors, dynamically recalibrates the model of the well in real time based on the new data, and calculates updated fluid flow properties of the fluid based on the recalibrated model. Continuing with the foregoing example, the real-time well operation system, in response to receiving new data, utilizes the recalibrated model to obtain the current flow rate, fluid composition, flow direction, and other fluid flow data at the location. Although the foregoing example describes performing operations to obtain the current flow rate, fluid composition, flow direction, and other fluid flow data at one location, the real-time well operation system is configured to perform operations described herein to simultaneously or near simultaneously obtain the current flow rate, fluid composition, flow direction, and other fluid flow data at multiple locations at or near the well, and/or throughout the entire well. In some embodiments, the real-time well operation system dynamically calculates and re-calculates an up-to-date map of the fluid flowing through a location, a section of the well, and/or through the entirety of the well

In some embodiments, the real-time well operation system, in response to receiving new data streamed from one or more sensors, determines whether a characteristic of the fluid flow (e.g., the flow rate, fluid density, fluid composition, fluid pressure, or another quantifiable characteristic of the fluid flow of the fluid) has changed over a threshold period of time (e.g., one minute, one hour, one day, or another period of time). In one or more of such embodiments, the real-time well operation system dynamically recalibrates the model in real time based on the change in the characteristic of the fluid flow over the period of time. For example, the real-time well operation system continuously monitors the flow rate (first characteristic) and a fluid composition (second characteristic) of a fluid flowing through a port positioned near a production zone over a period of time. Moreover, the real-time well operation system, in response to a determination that the flow rate has increased or decreased by more than a threshold rate or a threshold range within the last hour, day, week, or another period of time, dynamically recalibrates the model in real time to account for the change in the flow rate. Similarly, the real-time well operation system, in response to a determination that the fluid composition of the fluid has changed by more than a threshold amount (e.g., the composition of hydrocarbon sources within the fluid has increased or decreased by more than a threshold percentage) within the last hour, day, week, or another period of time, dynamically recalibrates the model in real-time to account for the change in the fluid composition of the fluid. In some embodiments, the real-time well operation system continuously and dynamically monitors for changes to one or more calibration coefficients of the model, and in response to a determination that the change is greater than a threshold value, dynamically recalibrates the model in real time. For example, where a calibration coefficient defines a pressure difference across an inflow control device, the real-time well operation system dynamically calculates the calibration coefficient based on the newest data obtained by the sensors. Further, the real-time well operation system, in response to a determination that the most recently calculated calibration coefficient varies from the calibration coefficient used by the model by more than a threshold value, applies the most recently calculated calibration coefficient to dynamically recalibrate the model.

In some embodiments, where the recalibrated model is not an existing digital twin of the well, the real-time well operation system compares the recalibrated model with the digital twin of the well to determine whether the digital twin matches the recalibrated model. In one or more of such embodiments, and in response to a determination that a property of the recalibrated model (e.g., net fluid flow out of a location, composition of fluid flowing at the location, or another property of the recalibrated model) varies from a corresponding property of the digital twin by more than a threshold range, dynamically updates the digital twin based on the recalibrated model. In one or more of such embodiments, the most recently recalibrated model is saved as the digital twin.

In some embodiments, the real-time well operation system continuously and dynamically monitors for changes to one or more characteristics of the fluid flow or one or more characteristics of the model to determine whether the changes are within an acceptable threshold range. In one or more of such embodiments, the real-time well operation system also performs the operations described herein to dynamically recalibrate the model while the characteristics of the fluid flow or the model remain within the acceptable threshold range. In one or more of such embodiments, the real-time well operation system, in response to a determination that the output is not within a threshold range, requests a manual recalibration of the model, or requests a manual review of the model by an operator. For example, where fluid flow through a production zone is initially 1,000 barrels of oil and 0 barrels of water over a period of time, and the acceptable threshold range is less than 100 barrels of water over the period of time, the real-time well operation system dynamically recalibrates the model while less than 100 barrels of water flow through the production zone over the period of time. However, the real-time well operation system alerts the operator and requests the operator to review the model to manually recalibrate the model or make manual adjustments to one or more parameters of the model to account for the increase in the flow of water through the production zone beyond the threshold range. In some embodiments, the real-time well operation system, in response to a determination that the output is not within a threshold range, dynamically recalibrates the model to fit the model within the threshold range, or recalibrates the model to fit the model within the threshold range if manual recalibration is not performed within a threshold period of time.

In some embodiments, the real-time well operation system determines one or more directions of the fluid flow at a location. In some embodiments, the real-time well operation system, in response to receiving the new data, dynamically recalibrates the model in real time based on the new data and determines the flow direction of the fluid flowing through the location of the well based on the recalibrated model. As referred to herein, a direction of fluid flow refers not only to the net fluid flow in a single direction, but also different directions of fluid flow at a location or zone, or different directions of fluid flow into and out of various hardware components and devices that are positioned along or near the well. For example, where 1,190 barrels of oil of 1,200 barrels of oil that flow from a formation into a production zone flow out of the production zone and towards the surface, and 10 of the 1,200 barrels of oil flow into an adjacent production zone that is further downhole from the production zone of interest, the net fluid flow out of the production zone towards the surface is 1,190 barrels of oil, however, the net fluid flow does not indicate the total amount of oil actually produced at the production zone due to cross-flow or dumping. The real-time well operation system is configured to not only determine the direction of net fluid flow at a location, but also fluid flow in multiple directions at the location. In one or more of such embodiments, the real-time well operation system determines, based on the direction of fluid flow, the presence of a malfunctioning device, such as a leaky seal that is causing fluid to flow into an adjacent production zone.

In some embodiments, the real-time well operation system dynamically analyzes current data and previously received data from a pattern associated with the fluid flow of the fluid. For example, the real-time well operation system is configured to recognize existing and new fluid flow patterns such as continuous flow out of the production zone, fluid composition of the fluid flowing out of the production zone, and other patterns associated with the fluid flow or one or more properties of the fluid flow. The real-time well operation system determines a change to the pattern associated with the fluid flow and, in response to a determination that the change to the pattern is greater than a threshold, dynamically recalibrates the model in real time. Continuing with the previous example, where the real-time well operation system recognizes that the fluid composition of the fluid flowing out of the production zone is approximately 80% oil and 20% water for the past month, the real-time well operation system, in response to a determination that the fluid composition of water has increased to more than 25% (or another threshold), dynamically recalibrates the model in real time to update the model.

In some embodiments, the real-time well operation system predicts fluid flow of the fluid at a future time or during a threshold period of time in the future based on the model. In one or more of such embodiments, the real-time well operation system dynamically analyzes current data and previously received data for a pattern associated with the fluid flow of the fluid. The real-time well operation system then generates a prediction of the fluid flow at the future time or during the threshold period of time in the future based on the pattern. In some embodiments, the real-time well operation system dynamically updates the pattern of fluid flow and generates updated predictions of fluid flow at the future time or during the threshold period of time in the future based on the most recently determined pattern of fluid flow. In one or more of such embodiments, the real-time well operation system also improves and/or optimizes existing predictions of fluid flow at the future time or during the threshold period of time in the future based on the most recently determined pattern of fluid flow.

In some embodiments, the real-time well operation system projects a fluid flow at a location, such as the wellhead of the well, and obtains measured fluid flow at the location. The real-time well operation system compares the projected fluid flow with the measured fluid flow, and dynamically recalibrates the model in real time in response to a determination that the projected fluid flow and the measured fluid flow at the location vary by more than a threshold. In one or more of such embodiments, the real-time well operation system also recalculates the fluid flow at other locations of the well or near the well based on the recalibrated model. In some embodiments, the real-time well operation system, after receiving new data, determines whether there is a change to one or more parameters of the model over a period of time, and dynamically recalibrates the model in real time based on a change to the one or more parameters.

The real-time well operation system is also configured to perform real-time analysis of a well operation, including determining the presence of malfunctioning hardware components. More particularly, the real-time well operation system determines, based on data streamed from the sensors disposed in a well, a presence of a malfunctioning hardware disposed in the well, where the data is associated with parameters of a digital twin of the well. In some embodiments, the real-time well operation system analyzes current data and data previously streamed over a threshold period of time (e.g., within the last hour, the last day, the last week, etc.) to determine whether a hardware component has malfunctioned. The real-time well operation system determines whether a property of the digital twin is within a threshold range. Further, the real-time well operation system, in response to a determination that the model is not within the threshold range, determines an improvement to the property of the digital twin, and dynamically recalibrates the digital twin in real time based on the data streamed from the plurality of sensors to improve the property of the digital twin. In some embodiments, the real-time well operation system predicts, based on the digital twin, fluid flow of fluid flowing through the well at a future time or within a period of time in the future. In some embodiments, the real-time well operation system utilizes the digital twin to perform a fluid flow simulation of a fluid flowing through the well to determine how to improve fluid flow of the fluid. In one or more of such embodiments, the real-time well operation system determines an improvement to an existing configuration of the hardware to improve the fluid flow of the fluid. The real-time well operation system then provides a recommendation to reconfigure the hardware to improve the fluid flow of the fluid. In some embodiments, the real-time well operation system is configured to analyze historical and/or live data indicative of sensor and hardware operations, and to replace and/or supplement missing data from one or more malfunctioning sensors or hardware. In one or more of such embodiments, the real-time well operation system analyzes historical and/or live data from functioning sensors and hardware and estimates data that would have been transmitted by one or more malfunctioning sensors or hardware (had the malfunction not occurred) to supplement and/or replace the missing data.

Although the foregoing paragraphs describe operations performed by the real-time well operation system to determine fluid flow at a location, the real-time well operation system is configured to simultaneously determine fluid flow at multiple locations. Similarly, although the foregoing paragraphs describe operations performed by the real-time well operation system to determine the presence of a malfunctioning hardware, the real-time well operation system is configured to simultaneously determine the presence of multiple malfunctioning hardware, such as multiple malfunctioning sensors and/or valves. Similarly, although the foregoing paragraphs describe operations performed by the real-time well operation system to provide a recommendation to improve fluid flow, the real-time well operation system is configured to simultaneously provide multiple recommendations to improve fluid flow at different locations, improve different aspects of a well operation, provide multiple forecasts of well operations of the well, and dynamically receive user inputs and configurations. Additional descriptions of the foregoing methods to perform real-time analysis of fluid flow in a well, methods to perform real-time analysis of a well operation, and real-time well operation systems are described in the paragraphs below and are illustrated in FIGS. 1-7 .

Turning now to the figures, FIG. 1 is a schematic, side view of a well environment 100 in which a real-time well operation system 118 is deployed near a surface 108 of a well 102. As shown in FIG. 1 , a wellbore 114 of well 102 extends from surface 108 of well 102 to or through formation 126. Completion 116 is coupled to or includes one or more ports, valves, and other devices that permit fluids (such as hydrocarbon resources and reservoir fluids) to flow from formation 126 through perforations 121A-121C into completion 116, and via completion 116 back toward surface 108. To that end, a diverter or an outlet conduit 128 may be connected to a container 130 at the wellhead 106 to provide a fluid return flow path from wellbore 114.

In the embodiment of FIG. 1 , completion 116 extends through three zones 111A, 111B, and 111C. Moreover, packer assemblies 110A-110C and other hardware components (not shown), and sensors 131A-131C and 141A-141C and other sensors (not shown) are also run downhole with completion 116, and are deployed or activated to isolate zones 111A, 111B, and 111C, control fluid flow into and out of zones 111A, 111B, and 111C, and measure and stream fluid flow data, and data indicative of the status of the hardware components deployed inside wellbore 114, at or near wellbore 114, and near surface 108, and data indicative of the well operation via telemetry to real-time well operation system 118.

Real-time well operation system 118 receives data streamed from sensors 131A-131C and 141A-141C and populates an initial model of fluid flow into zones 111A-111C based on the streamed data. In the embodiment of FIG. 1 , sensors 131A-131C are positioned outside of completion 116, whereas sensors 141A-141C are positioned inside of completion 116. Further, each pair of sensors 131A and 141A, 131B and 141B, and 131C and 141C are configured to measure fluid pressure, measure data indicative of fluid flow, and/or measure data that is derived to determine fluid flow at or near their corresponding locations. The measurements are utilized by real-time well operation system 118 as input parameters to populate the initial model of well 102 that provides a virtual representation of different aspects of well 102, including fluid flow into zones 111A-111C, net fluid flow out of each of zones 111A-111C, direction of fluid flow at different locations within zones 111A-111C, fluid flow at wellhead 106, fluid composition of fluids flowing into and out of zones 111A-111C, fluid pressure of fluids in zones 111A-111C, other fluid flow properties, and other aspects of well 102 and well operations performed at well 102. In some embodiments, as sensors 131A-131C and 141A-141C and other sensors continuously provide data to real-time well operation system 118, real-time well operation system 118 utilizes the updated data to recalibrate the model. In the embodiment of FIG. 1 , sensors 131D and 141D are positioned at or near wellhead 106 to measure the actual fluid flow (e.g., the actual flow rate, the actual fluid pressure, or other metrics of the fluids) of fluids flowing at wellhead 106.

In some embodiments, where the model projects fluid flow of fluids flowing at wellhead 106, real-time well operation system 118 compares the projected fluid flow with the fluid flow measured by sensors 131D and 141D, and dynamically adjusts the model if the projected fluid flow differs from the measured fluid flow by more than a threshold value. In some embodiments, real-time well operation system 118 continuously and dynamically monitors for changes to one or more characteristics of the fluid flow or the model to determine whether the changes are within an acceptable threshold range. In one or more of such embodiments, real-time well operation system 118 also performs the operations described herein to dynamically recalibrate the model while the characteristics of the fluid flow or characteristics of the model remain within the acceptable threshold range. In one or more of such embodiments, real-time well operation system 118, in response to a determination that the output is not within a threshold range, requests a manual recalibration of the model, or requests a manual review of the model by an operator. In some embodiments, real-time well operation system 118 dynamically analyzes data obtained from sensors 131A-131C and 141A-141C to project fluid flow in zones 111A, 111B, or 111C at a future date or during a period of time in the future.

Although FIG. 1 illustrates three pairs of sensors 131A and 141A, 131B and 141B, and 131C and 141C disposed in zones 111A, 111B, and 111C, respectively, in some embodiments, a different number and type of sensors are disposed across zones 111A, 111B, and 111C. Further, although the foregoing paragraphs describe real-time well operation system 118 utilizing data obtained from sensors 131A and 141A, 131B and 141B, and 131C and 141C to populate and update models of well 102, in some embodiments, real-time well operation system 118 is also configured to utilize data obtained from other sensors (not shown) and hardware components (not shown), data indicative of the well trajectory, data indicative of the fluid composition (PVT), data indicative of the reservoir information, and data indicative of other aspects of well 102 and components and hardware disposed in or around well 102 to populate and update the models. Further, although FIG. 1 illustrates completion 116 deployed across three zones 111A-111C, in some embodiments, completion 116 is deployed across a different number of zones. Further, although FIG. 1 illustrates a completion operation, real-time well operation system 118 is deployable during other well operations including, but not limited to, production operations, injection operations, and other types of well operations to perform real-time analysis of fluid flow, real-time analysis of the well operation, and to perform other operations described herein. Further, although FIG. 1 illustrates real-time well operation system 118 as a surface-based system, in some embodiments, real-time well operation system 118 is positioned in a downhole location- or is a cloud-based system. In some embodiments, real-time well operation system 118 is deployed near a subsea well and configured to perform operations described herein to populate and recalibrate a model of the subsea well. Additional descriptions of real-time well operation system 118 and operations performed by real-time well operation system 118 or by similar systems are provided herein and are illustrated in at least FIGS. 3-7 .

FIG. 2 is an illustration of a network 200 of nodes positioned along multiples layers of a zone of a well, such as one of zones 111A, 111B, or 111C of FIG. 1 , where each node represents a location at or around the well. In the embodiment of FIG. 2 , network 200 includes series of nodes along a first annulus layer 210 including nodes 212 and 214, a second annulus layer 220 including nodes 222 and 224, a third annulus layer 230 including nodes 232 and 234, a fourth annulus layer 240 including nodes 242 and 244, a reservoir layer 250 including nodes 252 and 254, and a tubing layer 260 including nodes 262 and 264, where a sensor is positioned at a corresponding location of the node. A real-time well operation system, such as real-time well operation system 118 of FIG. 1 , utilizes the flow data obtained by the sensors that are deployed at the nodes to determine the fluid flow properties (such as the flow rate, direction of fluid flow, fluid pressure, and other aspects of fluid flow) at each node, to populate a model of the zone of well, and to dynamically recalibrate the model based on up-to-date data streamed from the sensors. In some embodiments, the real-time well operation system auto-correlates fluid data obtained by sensors positioned at different nodes (such as nodes 232 and 234) and compares the different fluid data obtained by the different sensors to dynamically recalibrate the model. In some embodiments, the real-time well operation system utilizes one or more calibration coefficients to correlate different readings obtained from different sensors that are positioned at different locations, and to compensate for the different readings due to the placement and locations of the sensors. In one or more of such embodiments, the real-time well operation system periodically or dynamically adjusts one or more values of the calibration coefficients based on one or more changes to the fluid flow, changes to one or more sensors, and/or changes to one or more hardware components of the well.

Although FIG. 2 illustrates a set of nodes positioned along six layers in a zone, in some embodiments, network 200 includes a different number of nodes that are positioned along a different number of layers within the zone. Further, although FIG. 2 illustrates nodes along a single zone, the real-time well operation system is configured to perform operations described herein to simultaneously determine the fluid flow at nodes across multiple zones of the well, populate a model of the well across multiple zones, auto-correlate sensor readings across multiple nodes, and dynamically recalibrate the model based on up-to-date data streamed from the sensors positioned across multiple zones.

FIG. 3 is a flow chart of a process 300 to perform a real-time analysis of fluid flow in a well, and a real-time analysis of a well operation. At block 302 well project is created. At block 304, a well digital twin is initialized. Moreover, at blocks 338 and 334, a well simulator utilizes the well model data to initialize the well digital twin. At block 306, the well digital twin is populated with real-time data. More particularly, real-time data obtained by sensors at block 336 are validated at block 332, before the real-time data is utilized as input parameters to populate the well digital twin. At block 308, a simulation of the well is run. At block 310, data validation of the well is outputted. At block 312, and in response to a determination that the output is valid, process 300 moves to block 316. Alternatively, at block 312, and in response to determination that the output is not valid, process 300 moves to block 324. At block 316, the output data is published. At block 314, the output data is provided via a machine communication interface to another electronic device, such as an electronic device of an operator. At block 318, the inputs and outputs of the data are stored to a database, and process 300 moves to block 320, where the next record is accessed, and process 300 returns to block 336. At block 318, after the inputs and outputs of the data are stored in the database, process 300 also proceeds to block 322, where data analysis and decision-making processes are performed. Process 300 then loops back from block 322 to block 316, where the output data is published.

At block 324, an automated calibration process described herein is performed. Process 300 then proceeds to block 326, and, in response to a determination that calibration is successful, process 300 moves to block 330, and the new calibrated data is used. Alternatively, at block 326, and, in response to a determination that the calibration is not successful, process 300 moves to block 328, and a request for manual calibration is made, and the manually-calibrated data is used at block 330. Process 300 then proceeds to block 334, where the existing well model data is updated or replaced with the newly-calibrated data.

FIG. 4 is a block diagram of a real-time well operation system 400 that is similar to real-time well operation system 118 of FIG. 1 , and that is configured to perform the operations illustrated in process 600 of FIG. 6 . Real-time well operation system 400 includes a storage medium 406 and a processor 410. The storage medium 406 may be formed from data storage components such as, but not limited to, read-only memory (ROM), random access memory (RAM), flash memory, magnetic hard drives, solid state hard drives, CD-ROM drives, DVD drives, floppy disk drives, as well as other types of data storage components and devices. In some embodiments, the storage medium 406 includes multiple data storage devices. In further embodiments, the multiple data storage devices may be physically stored at different locations. In one of such embodiments, the data storage devices are components of a server station, such as a cloud server. Fluid flow data, data indicative of the status of hardware components and devices that are positioned in or near the well, and data indicative of the status of one or more well operations that are performed at the well fluid flow data (collectively “well data”) are stored at a first location 420 of storage medium 406. Further, instructions to receive data simultaneously streamed from a plurality of sensors disposed in a well are stored at a second location 422 of storage medium 406. Further, instructions to populate a model of the well with the data, where the model includes a plurality of parameters that are inputs of the model are stored at a third location 424 of storage medium 406. Further, instructions to calculate a fluid flow of a fluid flowing through a location of the well based on the model are stored at a fourth location 426 of storage medium 406. Further, in response to receiving new data streamed from one or more sensors of the plurality of sensors, instructions to dynamically recalibrate the model in real time based on the new data are stored at a fifth location 428 of storage medium 406. Further, in response to receiving new data streamed from one or more sensors of the plurality of sensors, instructions to calculate an updated fluid flow of the fluid flowing through the location of the well based on the recalibrated model are stored at a sixth location 430 of storage medium 406. Further, additional instructions that are performed by the processor 410 are stored in other locations of the storage medium 406.

FIG. 5 is a block diagram of a real-time well operation system 500 that is similar to real-time well operation system 118 of FIG. 1 , and is configured to perform the operations illustrated in process 700 of FIG. 7 . Real-time well operation system 500 includes a storage medium 506 and a processor 510 that are similar or identical to storage medium 406 and processor 410 of FIG. 4 . Well data is stored at a first location 520 of storage medium 506. Further, instructions to determine, based on data initially streamed from a plurality of sensors disposed in a well, a presence of a malfunctioning hardware disposed in the well are stored at a second location 522 of storage medium 506. Further, instructions to determine whether a property of the model is within a threshold range are stored at a third location 524 of storage medium 506. Further, and in response to a determination that the model is not within the threshold range, instructions to determine an improvement to the property of the model are stored at a fourth location 526 of storage medium 506. Further, and in response to a determination that the model is not within the threshold range, instructions to dynamically recalibrate the model in real time based on the data streamed from the plurality of sensors to improve the property of the model are stored at a fifth location 528 of storage medium 506. Further, additional instructions that are performed by the processor 510 are stored in other locations of the storage medium 506.

FIG. 6 is a flow chart of a process 600 to perform a real-time analysis of a well operation. Although the operations in process 600 are shown in a particular sequence, certain operations may be performed in different sequences or at the same time where feasible. As described below, process 600 provides an intuitive way for determining an activity associated with an object of interest.

At block S602, data simultaneously streamed from a plurality of sensors that are disposed in a well are received. For example, real-time well operation system 118 of FIG. 1 receives from sensors 131A-131C and 141A-141C of FIG. 1 , fluid flow data indicative of fluid flow at or near the corresponding locations of 131A-131C and 141A-141C. At block S604, a model of the well is populated based on the data. In the embodiment of FIG. 1 , real-time well operation system 118 is configured to populate the model while the data is being simultaneously streamed by sensors 131A-131C and 141A-141C.

At block S606, fluid flow of a fluid flowing through a location of the well is calculated based on the model. Continuing with the foregoing example, real-time well operation system 118 of FIG. 1 utilizes the model of the well to determine the fluid flow at a desired location or at other locations of the well. At block S608, in response to not receiving any new data streamed from the one or more sensors, process 600 proceeds to block S613. Alternatively, at block S608, and in response to receiving new data streamed from the one or more sensors, process 600 proceeds to block S610, and the model is dynamically recalibrated in real time based on the new data. Continuing with the foregoing example, real-time well operation system 118 continuously receives new data from sensors 131A-131C and 141A-141C of FIG. 1 , and, in response to receiving the new data, dynamically recalibrates the model of the well based on the new data. At block S612, an updated fluid flow of the fluid flowing through the location of the well is calculated based on the recalibrated model. Continuing with the foregoing example, real-time well operation system 118 utilizes the recalibrated model to determine the fluid flow at the desired location or other locations of the well. Process 600 then proceeds to block S613 and a determination of whether to continue the recalibration operation is made. Process 600 returns to block S608 in response to a determination to continue the recalibration operation. Alternatively, process 600 ends if a determination not to continue the recalibration operation is made.

FIG. 7 is a flow chart of a process 700 to perform a real-time analysis of a well operation. Although the operations in process 700 are shown in a particular sequence, certain operations may be performed in different sequences or at the same time where feasible. As described below, process 700 provides an intuitive way for determining an activity associated with an object of interest.

At block S702, a presence of a malfunctioning hardware disposed in a well is determined based on data streamed from a plurality of sensors that are disposed in a well. For example, real-time well operation system 118 of FIG. 1 is configured to analyze or run a digital twin of the well, where the data is associated with one or more parameters of the digital twin. More particularly, real-time well operation system 118 analyzes the parameters and/or outputs of the digital twin to determine the presence of a malfunctioning hardware, including, but not limited to, leaky valve, a valve having an incorrect opening or closed position, malfunctioning pressure gauge, and/or another malfunctioning hardware component disposed at or near the well. At block S704, a determination of whether a property of the model is within a threshold range is made. Continuing with the foregoing example, real-time well operation system 118 periodically or continuously determines whether one or more properties, characteristics, coefficients, and/or outputs of the model is not within a predefined threshold range. At block S706, a determination of whether the model is not within a threshold range is made. Process 700 proceeds to block S711 in response to a determination that the property of model is within the threshold range. Alternatively, process 700 proceeds to block S708 in response to a determination that the property of the model is indeed not within the threshold range. At block S708, an improvement to the property of the model is determined. At block S710, the model is dynamically recalibrated in real time based on the data streamed from the plurality of sensors to improve the property of the model. Process 700 then proceeds to block S711, and a determination of whether to continue process 700 is determined. Process 700 ends in response to a determination not to continue. Alternatively, process 700 returns to block S702 in response to a determination to continue.

The above-disclosed embodiments have been presented for purposes of illustration and to enable one of ordinary skill in the art to practice the disclosure, but the disclosure is not intended to be exhaustive or limited to the forms disclosed. Many insubstantial modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the disclosure. For instance, although the flowcharts depict a serial process, some of the steps/processes may be performed in parallel or out of sequence, or combined into a single step/process. The scope of the claims is intended to broadly cover the disclosed embodiments and any such modification. Further, the following clauses represent additional embodiments of the disclosure and should be considered within the scope of the disclosure.

Clause 1, a computer-implemented method to perform a real-time analysis of fluid flow in a well, comprising: receiving data simultaneously streamed from a plurality of sensors disposed in a well; populating a model of the well with the data, wherein the model comprises a plurality of parameters that are inputs of the model, and wherein each parameter of the plurality of parameters is associated with data streamed from a sensor of the plurality of sensors; calculating a fluid flow of a fluid flowing through a location of the well based on the model; and in response to receiving new data streamed from one or more sensors of the plurality of sensors: dynamically recalibrating the model in real-time based on the new data; and calculating an updated fluid flow of the fluid flowing through the location of the well based on the recalibrated model.

Clause 2, the computer-implemented method of clause 1, wherein populating the model comprises populating the model while the data is being simultaneously streamed from the plurality of sensors, and wherein calculating the fluid flow comprises calculating the fluid flow while the data is being simultaneously streamed from the plurality of sensors.

Clause 3, the computer-implemented method of clauses 1 or 2, wherein calculating the fluid flow of the fluid flowing through the location comprises calculating the fluid flow through the location before the data is stored on a storage medium, and wherein calculating the updated fluid flow of the fluid flowing through the location comprises calculating the fluid flow of the fluid flowing through the location before the updated data is stored on the storage medium.

Clause 4, the computer-implemented method of any of clauses 1-3, wherein, in response to receiving the new data, the method further comprising: determining a change in a parameter of the plurality of parameters over a period of time; and dynamically recalibrating the model in real time based on the change in the parameter over the period of time.

Clause 5, the computer-implemented method of any of clauses 1-4, wherein, in response to receiving the new data, the method further comprising: determining a change in a characteristic of the fluid flow over a period of time; and dynamically recalibrating the model in real time based on the change in the characteristic of the fluid flow over the period of time.

Clause 6, the computer-implemented method of any of clauses 1-5, further comprising: determining if a model is within a threshold range; and in response to a determination that the output is not within a threshold range, dynamically recalibrating the model to fit the model within the threshold range. Clause 7, the computer-implemented method of any of clauses 1-5, further comprising:

determining if a model is within a threshold range; and in response to a determination that the output is not within a threshold range, requesting a manual recalibration of the model.

Clause 8, the computer-implemented method of any of clauses 1-7, wherein the data and the new data comprise data indicative of at least one of a flow rate, a pressure, and a derivative of the pressure over time at the location.

Clause 9, the computer-implemented method of any of clauses 1-8, further comprising: determining a change in a coefficient of the model; and in response to a determination that the change is greater than a threshold value, dynamically recalibrating the model in real time.

Clause 10, the computer-implemented method of any of clauses 1-9, further comprising: projecting a first flow rate at a wellhead based on the model; determining a second flow rate that is measured at the wellhead; comparing the first flow rate and the second flow rate; and in response to a determination that the first flow rate and the second flow rate vary by more than a threshold; dynamically recalibrating the model in real time; and calculating an updated fluid flow of the fluid flowing through the location of the well based on the recalibrated model.

Clause 11, the computer-implemented method of any of clauses 1-10, further comprising: projecting a first pressure at a wellhead based on the model; determining a second pressure that is measured at the wellhead; and in response to a determination that the first pressure and the second pressure vary by more than a threshold; dynamically recalibrating the model in real time; and calculating an updated fluid flow of the fluid flowing through the location of the well based on the recalibrated model.

Clause 12, the computer-implemented method of any of clauses 1-11, further comprising: analyzing the data and previously received data from the plurality of sensors for a pattern associated with the fluid flow of the fluid; determining a change to the pattern associated with the fluid flow; and in response to a determination that the change to the pattern is greater than a threshold, dynamically recalibrating the model in real time.

Clause 13, the computer-implemented method of any of clauses 1-12, further comprising: determining a flow direction of the fluid flowing through the location of the well based on the model; and in response to receiving the new data: dynamically recalibrating the model in real time based on the new data; and determining the flow direction of the fluid flowing through the location of the well based on the recalibrated model.

Clause 14, the computer-implemented method of any of clauses 1-13, further comprising: predicting the fluid flow of the fluid within a threshold period of time based on the model; and in response to receiving the new data, predicting the fluid flow of the fluid within the threshold period of time based on the new data.

Clause 15, the computer-implemented method of any of clauses 1-14, wherein properties of one or more hardware components of the well are parameters of the plurality of parameters, and wherein populating the model of the well with the data comprises populating the model of the well based on the properties of the one or more hardware components.

Clause 16, the computer-implemented method of any of clauses 1-15, further comprising: after recalibrating the model, comparing the model with a digital twin of the well; and in response to a determination that a property of the model and a corresponding property of the digital twin vary by a threshold range, updating the digital twin based on the model.

Clause 17, the computer-implemented method of any of clauses 1-16, wherein calculating the fluid flow comprises calculating a flow rate of a hydrocarbon resource at the location, and wherein calculating the updated fluid flow comprises calculating the updated flow rate of the hydrocarbon resource at the location.

Clause 18, the computer-implemented method of any of clauses 1-17, further comprising: calculating a fluid flow map of fluid flow through a section of the well; and in response to receiving new data streamed from the one or more sensors of the plurality of sensors: calculating an updated fluid flow map of fluid flow through the section of the well.

Clause 19, a computer-implemented method to perform a real-time analysis of a well operation, comprising: determining, based on data streamed from a plurality of sensors disposed in a well, a presence of a malfunctioning hardware disposed in the well, wherein the data is associated with a plurality of parameters of a model of a well, the model being a digital twin of the well; determining whether a property of the model is within a threshold range; and in response to a determination that the model is not within the threshold range: determining an improvement to the property of the model; and dynamically recalibrating the model in real time based on the data streamed from the plurality of sensors to improve the property of the model.

Clause 20, the computer-implemented method of clause 19, wherein determining the presence of the malfunctioning hardware comprises determining, based on data streamed from the plurality of sensors over a period of time, the presence of the malfunctioning hardware.

Clause 21, the computer-implemented method of clauses 19 or 20, further comprising predicting, based on the model, a fluid flow of a fluid flowing through the well within a threshold period of time.

Clause 22, the computer-implemented method of any of clauses 19-21, further comprising: performing a fluid flow simulation of a fluid flowing through the well to determine an improvement to a fluid flow of the fluid; determining an improvement to an existing configuration of the hardware to improve the fluid flow of the fluid; and providing a recommendation to reconfigure the hardware.

Clause 23, a real-time well operation system, comprising: a storage medium; and one or more processors configured to: receive data simultaneously streamed from a plurality of sensors disposed in a well; populate a model of the well with the data, wherein the model comprises a plurality of parameters that are inputs of the model, and wherein each parameter of the plurality of parameters is associated with data streamed from a sensor of the plurality of sensors; calculate a fluid flow of a fluid flowing through a location of the well based on the model; and in response to receiving new data streamed from one or more sensors of the plurality of sensors: dynamically recalibrate the model in real time based on the new data; and calculate an updated fluid flow of the fluid flowing through the location of the well based on the recalibrated model.

Clause 24, the real-time well operation system of clause 23, wherein the one or more processors are further configured to: determine a change in a parameter of the plurality of parameters over a period of time; and dynamically recalibrate the model in real time based on the change in the parameter over the period of time.

As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprise” and/or “comprising,” when used in this specification and/or in the claims, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. In addition, the steps and components described in the above embodiments and figures are merely illustrative and do not imply that any particular step or component is a requirement of a claimed embodiment. 

What is claimed is:
 1. A computer-implemented method to perform a real-time analysis of fluid flow in a well, comprising: receiving data simultaneously streamed from a plurality of sensors disposed in a well; populating a model of the well with the data, wherein the model comprises a plurality of parameters that are inputs of the model, and wherein each parameter of the plurality of parameters is associated with data streamed from a sensor of the plurality of sensors; calculating a fluid flow of a fluid flowing through a location of the well based on the model; and in response to receiving new data streamed from one or more sensors of the plurality of sensors: dynamically recalibrating the model in real time based on the new data; and calculating an updated fluid flow of the fluid flowing through the location of the well based on the recalibrated model.
 2. The computer-implemented method of claim 1, wherein populating the model comprises populating the model while the data is being simultaneously streamed from the plurality of sensors, and wherein calculating the fluid flow comprises calculating the fluid flow while the data is being simultaneously streamed from the plurality of sensors.
 3. The computer-implemented method of claim 1, wherein calculating the fluid flow of the fluid flowing through the location comprises calculating the fluid flow through the location before the data is stored on a storage medium, and wherein calculating the updated fluid flow of the fluid flowing through the location comprises calculating the fluid flow of the fluid flowing through the location before the updated data is stored on the storage medium.
 4. The computer-implemented method of claim 1, wherein, in response to receiving the new data, the method further comprising: determining a change in a parameter of the plurality of parameters over a period of time; and dynamically recalibrating the model in real time based on the change in the parameter over the period of time.
 5. The computer-implemented method of claim 1, wherein, in response to receiving the new data, the method further comprising: determining a change in a characteristic of the fluid flow over a period of time; and dynamically recalibrating the model in real time based on the change in the characteristic of the fluid flow over the period of time.
 6. The computer-implemented method of claim 1, further comprising: determining if a model is within a threshold range; and in response to a determination that the output is not within a threshold range, dynamically recalibrating the model to fit the model within the threshold range.
 7. The computer-implemented method of claim 1, further comprising: determining if a model is within a threshold range; and in response to a determination that the output is not within a threshold range, requesting a manual recalibration of the model.
 8. The computer-implemented method of claim 1, wherein the data and the new data comprise data indicative of at least one of a flow rate, a pressure, and a derivative of the pressure over time at the location.
 9. The computer-implemented method of claim 1, further comprising: determining a change in a coefficient of the model; and in response to a determination that the change is greater than a threshold value, dynamically recalibrating the model in real time.
 10. The computer-implemented method of claim 1, further comprising: projecting a first flow rate at a wellhead based on the model; determining a second flow rate that is measured at the wellhead; comparing the first flow rate and the second flow rate; and in response to a determination that the first flow rate and the second flow rate vary by more than a threshold; dynamically recalibrating the model in real time; and calculating an updated fluid flow of the fluid flowing through the location of the well based on the recalibrated model.
 11. The computer-implemented method of claim 1, further comprising: projecting a first pressure at a wellhead based on the model; determining a second pressure that is measured at the wellhead; and in response to a determination that the first pressure and the second pressure vary by more than a threshold; dynamically recalibrating the model in real time; and calculating an updated fluid flow of the fluid flowing through the location of the well based on the recalibrated model. 20
 12. The computer-implemented method of claim 1, further comprising: analyzing the data and previously received data from the plurality of sensors for a pattern associated with the fluid flow of the fluid; determining a change to the pattern associated with the fluid flow; and in response to a determination that the change to the pattern is greater than a threshold, dynamically recalibrating the model in real time.
 13. The computer-implemented method of claim 1, further comprising: determining a flow direction of the fluid flowing through the location of the well based on the model; and in response to receiving the new data: dynamically recalibrating the model in real time based on the new data; and determining the flow direction of the fluid flowing through the location of the well based on the recalibrated model.
 14. The computer-implemented method of claim 1, further comprising: predicting the fluid flow of the fluid within a threshold period of time based on the model; and in response to receiving the new data, predicting the fluid flow of the fluid within the threshold period of time based on the new data.
 15. The computer-implemented method of claim 1, wherein properties of one or more hardware components of the well are parameters of the plurality of parameters, and wherein populating the model of the well with the data comprises populating the model of the well based on the properties of the one or more hardware components.
 16. The computer-implemented method of claim 1, further comprising: after recalibrating the model, comparing the model with a digital twin of the well; and in response to a determination that a property of the model and a corresponding property of the digital twin vary by a threshold range, updating the digital twin based on the model.
 17. The computer-implemented method of claim 1, wherein calculating the fluid flow comprises calculating a flow rate of a hydrocarbon resource at the location, and wherein calculating the updated fluid flow comprises calculating the updated flow rate of the hydrocarbon resource at the location.
 18. The computer-implemented method of claim 1, further comprising: calculating a fluid flow map of fluid flow through a section of the well; and in response to receiving new data streamed from the one or more sensors of the plurality of sensors: calculating an updated fluid flow map of fluid flow through the section of the well.
 19. A computer-implemented method to perform a real-time analysis of a well operation, comprising: determining, based on data streamed from a plurality of sensors disposed in a well, a presence of a malfunctioning hardware disposed in the well, wherein the data is associated with a plurality of parameters of a model of a well, the model being a digital twin of the well; determining whether a property of the model is within a threshold range; and in response to a determination that the model is not within the threshold range: determining an improvement to the property of the model; and dynamically recalibrating the model in real time based on the data streamed from the plurality of sensors to improve the property of the model.
 20. The computer-implemented method of claim 19, wherein determining the presence of the malfunctioning hardware comprises determining, based on data streamed from the plurality of sensors over a period of time, the presence of the malfunctioning hardware.
 21. The computer-implemented method of claim 19, further comprising predicting, based on the model, a fluid flow of a fluid flowing through the well within a threshold period of time.
 22. The computer-implemented method of claim 19, further comprising: performing a fluid flow simulation of a fluid flowing through the well to determine an improvement to a fluid flow of the fluid; determining an improvement to an existing configuration of the hardware to improve the fluid flow of the fluid; and providing a recommendation to reconfigure the hardware.
 23. A real-time well operation system, comprising: a storage medium; and one or more processors configured to: receive data simultaneously streamed from a plurality of sensors disposed in a well; populate a model of the well with the data, wherein the model comprises a plurality of parameters that are inputs of the model, and wherein each parameter of the plurality of parameters is associated with data streamed from a sensor of the plurality of sensors; calculate a fluid flow of a fluid flowing through a location of the well based on the model; and in response to receiving new data streamed from one or more sensors of the plurality of sensors: dynamically recalibrate the model in real time based on the new data; and calculate an updated fluid flow of the fluid flowing through the location of the well based on the recalibrated model.
 24. The real-time well operation system of claim 23, wherein the one or more processors are further configured to: determine a change in a parameter of the plurality of parameters over a period of time; and dynamically recalibrate the model in real time based on the change in the parameter over the period of time. 